Everything needed to prepare for Senior GenAI and RAG Engineer interviews — checklists, architecture reference, production scenarios, Python drills and deep-dive study modules.
Comprehensive checklist covering RAG, LangGraph agents, AWS Bedrock, vector databases, RAGAS evaluation and LoanIQ-specific concepts. Filterable by priority tier — must-know, good-to-have, and LoanIQ hands-on proof.
Full ecosystem reference — ingestion pipeline, retrieval stack, generation layer, agent orchestration, evaluation and observability. A single-page technical poster covering 22 sections.
Real-world production problems and curveball interview questions — hallucination handling, RAG failure modes, latency tuning, multi-agent debugging, and scaling GenAI systems. All answered from LoanIQ experience.
Python for GenAI interviews — async patterns, concurrency, data structures, system design principles, and production-grade coding practices. Includes worked examples and interview Q&A.
BM25, pgvector, hybrid search, RRF fusion, cross-encoder reranking, MMR — everything inside the retrieval layer with code walkthroughs and interview Q&A.
LLM generation layer, prompt engineering, RAGAS metrics — faithfulness, answer relevancy, context precision and recall — evaluation pipelines and CI quality gates.
Transformer internals, attention mechanisms, embeddings, fine-tuning vs RAG trade-offs, quantisation, and the ML theory underpinning every GenAI system.
From RAG chatbot to multi-agent underwriting system — a chronological deep-dive into every architectural decision, pivot, and lesson learned across all projects. Written as a first-person interview narrative.
Senior GenAI and RAG Engineer roles in India. Reach out via email, LinkedIn or WhatsApp.